scholarly journals Development of Compartment Models for Diagnostic Purposes

2021 ◽  
Vol 49 (1) ◽  
pp. 47-58
Author(s):  
Bálint Levente Tarcsay ◽  
Ágnes Bárkányi ◽  
Tibor Chován ◽  
Sándor Németh
Keyword(s):  
Fault Detection ◽  
Expert Systems ◽  
Human Life ◽  
Complex Model ◽  
Diagnostic Methods ◽  
Detection Methods ◽  
Environmental Damage ◽  
Financial Loss ◽  
Compartment Models ◽  
Box Models

The importance of recognizing the presence of process faults and resolving these faults is continuously increasing parallel to the development of industrial processes. Fault detection methods which are both robust and sensitive help to recognize the presence of faults in time to avoid malfunctions, financial loss, environmental damage or loss of human life. In the literature, the use of various model-based fault detection methods has gained a considerable degree of popularity. Methods usually based on black-box models, data-based techniques or models using symbolic logic, e.g.\ expert systems, have become widespread. White-box models, on the other hand, have been applied less despite their considerable robustness because of multiple reasons. Firstly, their complexity and the relatively vast amount of technological and modelling knowledge needed to construct them for industrial systems. Secondly, their large computational demand which makes them less suitable for online fault detection. In this study, the aim was to resolve these problems by developing a method to simplify the complex Computational Fluid Dynamics models employed to describe the equipment used in the chemical industry into less complex model structures. These simpler structures are Compartment Models, a type of white-box model which breaks down a complex system into smaller units with idealized behaviour. In the case of a small number of compartments, the computational load of such models is not significant, therefore, they can be employed for the purposes of online fault detection while providing an accurate representation of the system. For the purpose of identifying the compartmental structure, fuzzy logic was employed to create a model which approximates the real behaviour of the system as accurately as possible. Our future objective is to explore the possibility of combining this model with various diagnostic methods (expert systems, Bayesian networks, parity relations, etc.) and derive robust tools for the purpose of fault detection.

Defining the Environment

2017 ◽  
Author(s):  
Cymie R. Payne
Keyword(s):  
International Law ◽  
Human Life ◽  
Fundamental Aspect ◽  
Environmental Damage ◽  
Jus Post Bellum ◽  
Complex Concept ◽  
Scientific Analysis ◽  
Environmental Integrity ◽  
Healthy Functioning

The principle of ‘environmental integrity’ is a fundamental aspect of jus post bellum. Human life, economy, and culture depend on a healthy, functioning environment. However, environmental integrity is a complex concept to describe. Doctrinal thresholds for legally material environmental damage (significant, long-term, widespread) do not capture it. This chapter interrogates the jus post bellum literature and then turns to scholarship on wilderness management in the Anthropocene era, which also engages with the meaning of ‘environmental integrity’, ‘naturalness’, ‘unimpaired’, or, in the words of the Factory at Chorzów case which sets the international law standard for reparations of damage, ‘the situation which would, in all probability, have existed if that act had not been committed’. Recognition that pristine or historical conditions are often impossible to recover or maintain leads to the legal, ethical, and scientific analysis of evolving environmental norms that this chapter offers.

scholarly journals A Comparative Study on Fault Detection Methods for Gas Turbine Combustion Systems

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 389
Author(s):  
Jinfu Liu ◽  
Zhenhua Long ◽  
Mingliang Bai ◽  
Linhai Zhu ◽  
Daren Yu
Keyword(s):  
Fault Detection ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Operating Conditions ◽  
Detection Methods ◽  
Distribution Model ◽  
Outlet Temperature ◽  
Combustion System ◽  
Comparison Results ◽  
Gas Turbine Combustion

As one of the core components of gas turbines, the combustion system operates in a high-temperature and high-pressure adverse environment, which makes it extremely prone to faults and catastrophic accidents. Therefore, it is necessary to monitor the combustion system to detect in a timely way whether its performance has deteriorated, to improve the safety and economy of gas turbine operation. However, the combustor outlet temperature is so high that conventional sensors cannot work in such a harsh environment for a long time. In practical application, temperature thermocouples distributed at the turbine outlet are used to monitor the exhaust gas temperature (EGT) to indirectly monitor the performance of the combustion system, but, the EGT is not only affected by faults but also influenced by many interference factors, such as ambient conditions, operating conditions, rotation and mixing of uneven hot gas, performance degradation of compressor, etc., which will reduce the sensitivity and reliability of fault detection. For this reason, many scholars have devoted themselves to the research of combustion system fault detection and proposed many excellent methods. However, few studies have compared these methods. This paper will introduce the main methods of combustion system fault detection and select current mainstream methods for analysis. And a circumferential temperature distribution model of gas turbine is established to simulate the EGT profile when a fault is coupled with interference factors, then use the simulation data to compare the detection results of selected methods. Besides, the comparison results are verified by the actual operation data of a gas turbine. Finally, through comparative research and mechanism analysis, the study points out a more suitable method for gas turbine combustion system fault detection and proposes possible development directions.

The Role of Offshore Monitoring in an Effective Deepwater Riser Integrity Management Program

2007 ◽  
Author(s):  
Brittany Goldsmith ◽  
Elizabeth Foyt ◽  
Madhu Hariharan
Keyword(s):  
Failure Modes ◽  
Human Life ◽  
Management Program ◽  
Measured Data ◽  
Operating Conditions ◽  
Environmental Damage ◽  
Positioning Systems ◽  
Integrity Management ◽  
Deepwater Riser

As offshore field developments move into deeper water, one of the greatest challenges is in designing riser systems capable of overcoming the added risks of more severe environments, complicated well requirements and uncertainty of operating conditions. The failure of a primary riser component could lead to unacceptable consequences, including environmental damage, lost production and possible injury or loss of human life. Identification of the risks facing riser systems and management of these risks are essential to ensure that riser systems operate without failure. Operators have recognized the importance of installing instrumentation such as global positioning systems (GPS), vessel motion measurement packages, wind and wave sensors and Acoustic Doppler Current Profiler (ADCP) units to monitor vessel motions and environmental conditions. Additionally, high precision monitoring equipment has been developed for capturing riser response. Measured data from these instruments allow an operator to determine when the limits of acceptable response, predicted by analysis or determined by physical limitations of the riser components, have been exceeded. Regular processing of measured data through automated routines ensures that integrity can be quickly assessed. This is particularly important following extreme events, such as a hurricane or loop current. High and medium alert levels are set for each parameter, based on design analysis and operating data. Measured data is compared with these alert levels, and when an alert level is reached, further response evaluation or inspection of the components in question is recommended. This paper will describe the role of offshore monitoring in an integrity management program and discuss the development of alert levels based on potential failure modes of the riser systems. The paper will further demonstrate how this process is key for an effective integrity management program for deepwater riser systems.

RNN-based Fault Detection Method for MMC Photovoltaic Grid-connected System

2021 ◽  
Vol 14 ◽  
Author(s):  
Yuqi Pang ◽  
Gang Ma ◽  
Xiaotian Xu ◽  
Xunyu Liu ◽  
Xinyuan Zhang
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Circuit Breaker ◽  
Detection Methods ◽  
Multilevel Converters ◽  
Modular Multilevel Converters ◽  
Dc Circuit Breaker ◽  
Linear Elements ◽  
Hidden Layer ◽  
Analytical Expressions

Background: Fast and reliable fault detection methods are the main technical challenges faced by photovoltaic grid-connected systems through modular multilevel converters (MMC) during the development. Objective: Existing fault detection methods have many problems, such as the inability of non-linear elements to form accurate analytical expressions, the difficulty of setting protection thresholds and the long detection time. Method: Aiming at the problems above, this paper proposes a rapid fault detection method for photovoltaic grid-connected systems based on Recurrent Neural Network (RNN). Results: The phase-to-mode transformation is used to extract the fault feature quantity to get the RNN input data. The hidden layer unit of the RNN is trained through a large amount of simulation data, and the opening instruction is given to the DC circuit breaker. Conclusion: The simulation verification results show that the proposed fault detection method has the advantage of faster detection speed without difficulties in setting and complicated calculation.

scholarly journals Fault Detection Method Based on Global-Local Marginal Discriminant Preserving Projection for Chemical Process

Processes ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 122
Author(s):  
Yang Li ◽  
Fangyuan Ma ◽  
Cheng Ji ◽  
Jingde Wang ◽  
Wei Sun
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Chemical Process ◽  
Detection Method ◽  
Detection Performance ◽  
Detection Methods ◽  
Normal Operation ◽  
Discriminative Feature ◽  
Inherent Feature ◽  
Global And Local

Feature extraction plays a key role in fault detection methods. Most existing methods focus on comprehensive and accurate feature extraction of normal operation data to achieve better detection performance. However, discriminative features based on historical fault data are usually ignored. Aiming at this point, a global-local marginal discriminant preserving projection (GLMDPP) method is proposed for feature extraction. Considering its comprehensive consideration of global and local features, global-local preserving projection (GLPP) is used to extract the inherent feature of the data. Then, multiple marginal fisher analysis (MMFA) is introduced to extract the discriminative feature, which can better separate normal data from fault data. On the basis of fisher framework, GLPP and MMFA are integrated to extract inherent and discriminative features of the data simultaneously. Furthermore, fault detection methods based on GLMDPP are constructed and applied to the Tennessee Eastman (TE) process. Compared with the PCA and GLPP method, the effectiveness of the proposed method in fault detection is validated with the result of TE process.

scholarly journals Biosensors for the Determination of SARS-CoV-2 Virus and Diagnosis of COVID-19 Infection

2022 ◽  
Vol 23 (2) ◽  
pp. 666
Author(s):  
Maryia Drobysh ◽  
Almira Ramanaviciene ◽  
Roman Viter ◽  
Chien-Fu Chen ◽  
Urte Samukaite-Bubniene ◽  
...  
Keyword(s):  
Current Trend ◽  
Analytical Signal ◽  
Diagnostic Methods ◽  
Detection Methods ◽  
Label Free ◽  
Free Mode ◽  
Rna Hybridisation ◽  
Antigen Antibody ◽  
Affinity Interaction

Monitoring and tracking infection is required in order to reduce the spread of the coronavirus disease 2019 (COVID-19), induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To achieve this goal, the development and deployment of quick, accurate, and sensitive diagnostic methods are necessary. The determination of the SARS-CoV-2 virus is performed by biosensing devices, which vary according to detection methods and the biomarkers which are inducing/providing an analytical signal. RNA hybridisation, antigen-antibody affinity interaction, and a variety of other biological reactions are commonly used to generate analytical signals that can be precisely detected using electrochemical, electrochemiluminescence, optical, and other methodologies and transducers. Electrochemical biosensors, in particular, correspond to the current trend of bioanalytical process acceleration and simplification. Immunosensors are based on the determination of antigen-antibody interaction, which on some occasions can be determined in a label-free mode with sufficient sensitivity.

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